Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics, particularly data-independent acquisition (DIA), has become widely adopted across One Health approaches for biological and clinical research for quantitative protein characterization. Among the many computational tools available, DIA-NN has demonstrated superior performance; however, the primary output of the current versions is provided as a compact, compressed PARQUET file that can be difficult to interrogate without programming expertise. To address this limitation, we developed DIA-NN EasyFilter (DEF), a fast, user-friendly, KNIME-based workflow for comprehensive protein filtering and visualization. DEF integrates chromatographic peak-based filtering, curated contaminant libraries, and quantity-quality assessment along with interactive modules for qualitative and quantitative data exploration. The workflow is optimized for efficient execution within the KNIME local desktop environment and is designed to support end-users in improving accuracy and interpretability without requiring coding skills. We provide a detailed description on how to run DEF and demonstrate the utility and robustness of DEF using published large-scale proteomics data sets, showing high comparability across studies regardless of instrument platform or data set size.
Moagi et al. (2026) studied this question.